Prediction And Optimization Of Fracturing In Tight Gas And Shale Using A Coupled Geomechanical Model Of Combined Tensile And Shear Fracturing
Bibliographic record
Abstract
Abstract Hydraulic fracturing is a stimulation technique essential for economical development of tight gas and shale gas reservoirs. Analysis of the performance of fracturing jobs and optimization of the treatment design requires modeling which accounts for all important features of the process and ideally covers both the treatment and post-stimulation production of the well. It is now well established that the productivity of the wells is due not only to the classical tensile single plane fracture (SPF), but to the development of an enhanced permeability region (stimulated reservoir volume or SRV) around it due to shear fracturing and/or stimulation of existing dual porosity. The shape and size of the SRV depends not only on the injection process but also on the geomechanics of the reservoir. Current techniques are not able to predict its dependence on frac job parameters, which precludes any meaningful optimization. Typically the SRV size is assumed (e.g., from microseismic) in production forecasting. In this work we have developed a new coupled geomechanical and flow model for analysis and optimization of tight and shale gas treatments. The formulation includes the propagation of a tensile (SPF) fracture and dynamic development of the shear failure. Non-fractured blocks are assumed to be of linear elastic material; whereas in the failed blocks, fractures and rock compliance matrices are homogenized to form an equivalent compliance matrix. Simple Mohr-Coulomb and tensile failure relationships were used as the criteria for detecting fracture creation. Hyperbolic functions are used to describe the fracture normal and pre-peak shear deformations while the post-peak shear behavior follows an elasto-plastic model. The permeability enhancement during the fracturing process is computed and is the principal coupling between the flow and geomechanics. The model is 3-dimensional and treats both normal and shear behaviour of fractures. The simulation results reveal that shear fracturing will be the dominant fracturing mechanism in cases where the rock cohesion is low and the deviatoric stress is high, whereas tensile fracturing prevails in other conditions. The new model will be a realistic tool for analyzing the dependence of the well productivity on design parameters such as stage volume and pumping rate, spacing between stages, etc. It can be also used to screen shale plays for the most favorable geomechanical conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".